A Morphological Hilbert-Huang Transform Technique for Bearing Fault Detection

被引：84

作者：

Osman, Shazali ^{[1
]}

Wang, Wilson ^{[2
]}

机构：

[1] Lakehead Univ, Dept Elect & Comp Engn, Thunder Bay, ON P7B 5E1, Canada

[2] Lakehead Univ, Dept Mech Engn, Thunder Bay, ON P7B 5E1, Canada

来源：

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT | 2016年 / 65卷 / 11期

基金：

加拿大自然科学与工程研究理事会;

关键词：

Bearing fault detection; Hilbert-Huang transform; morphology filtering; ROLLER-BEARINGS; SIGNAL ANALYSIS; WAVELET FILTER; DIAGNOSIS; ELEMENT; MACHINE; SPECTRUM;

D O I：

10.1109/TIM.2016.2598019

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Most rotary machinery imperfections are related to defects in rolling element bearings. Unfortunately, reliable bearing fault detection still remains a challenging task, especially when bearing defect-related features are nonstationary. A new morphological Hilbert-Huang (MH) technique is proposed in this paper for incipient bearing fault detection. In the proposed MH technique, a new linearity measure method is suggested to demodulate characteristic feature functions, and a mathematical morphological filter is proposed to reduce impedance effect of the measured vibration signal to improve fault detection accuracy. The effectiveness of the proposed MH technique is verified by a series of experimental tests corresponding to different bearing conditions.

引用

页码：2646 / 2656

页数：11

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